Implementation of OSPOP, an algorithm for the estimation of optimal sampling times in pharmacokinetics by the ED, EID and API criteria

Tod, Michel; Rocchisani, Jean-Marie

doi:10.1016/0169-2607(96)01721-x

Cited by 37 publications

(21 citation statements)

References 10 publications

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“…In a nonlinear mixed effects setting this was introduced by Tod and Rocchisani [24] for the D-optimal design criterion by integrating the determinant of the population FIM with respect to the parameter distribution, i.e. ED-optimal design.…”

Section: Performance Of Serial Correlation Designs In Parameter Estimmentioning

confidence: 99%

Serial correlation in optimal design for nonlinear mixed effects models

Nyberg

Hoglund

Bergstrand

et al. 2012

J Pharmacokinet Pharmacodyn

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In population modeling two sources of variability are commonly included; inter individual variability and residual variability. Rich sampling optimal design (more samples than model parameters) using these models will often result in a sampling schedule where some measurements are taken at exactly the same time point, thereby maximizing the signal-to-noise ratio. This behavior is a result of not appropriately taking into account error generation mechanisms and is often clinically unappealing and may be avoided by including intrinsic variability, i.e. serially correlated residual errors. In this paper we extend previous work that investigated optimal designs of population models including serial correlation using stochastic differential equations to optimal design with the more robust, and analytic, AR(1) autocorrelation model. Further, we investigate the importance of correlation strength, design criteria and robust designs. Finally, we explore the optimal design properties when estimating parameters with and without serial correlation. In the investigated examples the designs and estimation performance differs significantly when handling serial correlation.

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Section: Performance Of Serial Correlation Designs In Parameter Estimmentioning

confidence: 99%

Serial correlation in optimal design for nonlinear mixed effects models

Nyberg

Hoglund

Bergstrand

et al. 2012

J Pharmacokinet Pharmacodyn

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show abstract

“…At least one of these criteria, the ELD criterion (expectation over the logarithm of the D-optimal design criterion for the population), does not have the property of replication. Tod and Rocchisani have also made inroads into this area (10,11). All the work in this area requires that one possess specialized software that is not widely available.…”

Section: Discussionmentioning

confidence: 99%

Optimal Sampling Schedule Design for Populations of Patients

Tam

Preston

Drusano

2003

Antimicrob Agents Chemother

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Generation of pharmacodynamic relationships in the clinical arena requires estimation of pharmacokinetic parameter values for individual patients. When the target population is severely ill, the ability to obtain traditional intensive blood sampling schedules is curtailed. Population modeling guided by optimal sampling theory has provided robust estimates of individual patient pharmacokinetic parameter values. Because of the wide range of parameter values seen in this circumstance, it is important to know how the range of parameter values in the population affects the timing of the optimal samples. We describe a new, simple technique to obtain optimal samples for a population of patients. This technique uses the nonparametric distribution associated with a nonparametric adaptive grid population pharmacokinetic analysis. We used the distribution from an analysis of 58 patients receiving levofloxacin for nosocomial pneumonia at a dose of 750 mg. The collection of parameter vectors and their associated probabilities were entered into a D-optimal design evaluation by using ADAPT II. The sampling times, weighted for their probabilities, were displayed in a frequency histogram (an expression of how system information varies with time for the population). Such an explicit expression of the time distribution of information allows rational sampling design that is robust not only for the population mean vector, as in traditional D-optimal design theory, but also for large portions of the total population. For levofloxacin, one reasonable six-sample design would be 1.5, 2, 2.25, 4, 4.75, and 24 h after starting a 90-min infusion. Such sampling designs allow informative population pharmacokinetic analysis with precise and unbiased estimates after the maximal a posteriori probability Bayesian step. This allows the highest probability of delineating a pharmacodynamic relationship.The goal of anti-infective agent chemotherapy is to give infected patients a dose of drug that has the highest possible probability of achieving the desired therapeutic end point (clinical response, microbiological response, suppression of resistance) while simultaneously having an acceptably low probability of generating a concentration-related adverse event. To achieve this goal, it is necessary, as a first step, to develop exposure-effect as well as exposure-toxicity relationships.While this has been difficult in the past, the advent of newer mathematical techniques such as population pharmacokinetic modeling, maximal a posteriori probability (MAP) Bayesian estimation and linkage to end points through modeling with logistic regression, and Cox proportional-hazard modeling has allowed the direct delineation of exposure-effect as well as exposure-toxicity relationships.Most of the data necessary for construction of such relationships (and the most expensive data) have already been collected as part of the Food and Drug Administration-mandated phase II/III clinical trial structure. For a case to be both clinically and microbiologically evaluable, it ...

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“…In [9,10] the corresponding designs are explored in population PK studies. This approach is implemented in such software packages as OSP-Fit by Tod and Roccisani [17], PFIM by Retout et al [18], and PopED by Foracchia et al [19].…”

Section: Discussionmentioning

confidence: 99%

D‐optimal designs for parameter estimation for indirect pharmacodynamic response models

Khinkis

Krzyzanski

Jusko

et al. 2003

Clin Pharma and Therapeutics

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This report generates efficient experimental designs (dose, sampling times) for parameter estimation for four basic physiologic indirect pharmacodynamic response (IDR) models. The principles underlying IDR models and their response patterns have been well described. Each IDR model explicitly contains four parameters, k in (production), k out (loss), I max /S max (capacity) and IC 50 /SC 50 (sensitivity). The pharmacokinetics of an IV dose of drug described by a monoexponential function of time with two parameters, V and k el , is assumed. The random errors in the response variable are assumed to be additive, independent, and normal with zero mean and variance proportional to some power of the mean response. Optimal design theory was used extensively to assess the role of both dose and sampling times. Our designs were generated in Mathematica (ADAPT 5 typically produces identical results). G-optimality was used to verify that the generated designs were indeed D-optimal. Such designs are efficient and robust when good prior knowledge of the estimated parameters is available. The efficiency of unconstrained Doptimal designs (4 dose, sampling time pairs) does not improve much when the drug doses are allowed to differ, compared with constrained single dose designs (4 sampling times) with one maximal feasible dose. Also, explored were efficiencies of alternative study designs and results from parameter misspecification. This analysis substantiates the importance of larger doses yielding greater certainty in parameter estimation in pharmacodynamics.

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Implementation of OSPOP, an algorithm for the estimation of optimal sampling times in pharmacokinetics by the ED, EID and API criteria

Cited by 37 publications

References 10 publications

Serial correlation in optimal design for nonlinear mixed effects models

Serial correlation in optimal design for nonlinear mixed effects models

Optimal Sampling Schedule Design for Populations of Patients

D‐optimal designs for parameter estimation for indirect pharmacodynamic response models

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